XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/5: METHODS FOR CHANGE DETECTION AND PROCESS MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ACCURACY IMPROVEMENT OF CHANGE DETECTION BASED ON COLOR ANALYSIS J. Wang, H. Koizumi, T. Kamiya

Document type:: Multivolume work

Structure type:: Chapter

3. METHODOLOGY The proposed framework for accuracy improvement of change detection aims at solving the problems of illumination change and location difference between orthoimages. After analyzing the mutual influence of the above problems, we design the following framework. Firstly, the global location difference between two orthoimages for change detection is rectified, which should be implemented at first especially when there exists overall large location difference. Secondly, the illumination change adjustment is carried out to make two orthoimages have more unified illumination condition. Thirdly, more precise rectification of location difference in local regions is performed, aiming at making the same object appear in the same location. This framework for accuracy improvement is implemented before the change detection as pre-processing, since the above three steps improve the quality of original input data for change detection. By implementing these steps to get more accurate input data, more accurate detection results and less processing time of change detection can be expected. 3.1 Global Location Difference Rectification As it is stated in the above, change detection is carried out on both orthoimages and DSMs from different times. For both orthoimages and DSMs, the location difference between two datasets may interfere with the detection accuracy of final results. The location difference between two datasets happens due to various different factors. For example, the aerial triangulation data used for generating DSM from stereo images may have different accuracy levels for two datasets. Or there are different systematic errors coming from camera, aerial triangulation calculation, and stereo matching. Rather than analyze the above reasons of the location difference, we decide to directly analyze the two datasets to find out the location error between them. For the two datasets for change detection, in the wide area shown in the images, most of the parts are not changing. For example, according to [4], the percentage of changing buildings annually in one photograph is only 3% to 5% of the overall number of buildings in most cases. Based on this, from another point of view, it is possible to carry out matching on the unchanging parts in two datasets from different time. Compared with DSM data including only height information, orthoimages have more information with three color channels and more characteristics are able to be extracted. Therefore it is easier to carry out matching between two orthoimages than DSM data. Through image matching, we attempt to find the location difference between the parts in respective orthoimage that describe the same area in the real world. Basically, orthoimages are ortho-rectified results of original aerial images based on 3D information of DSM. Strictly speaking, after ortho-rectification, each pixel in the orthoimage corresponds to only one point in the real world with unique latitude and longitude. In this sense, in the two orthoimages of different time, two corresponding points should be in the same location. But due to the errors stated in the beginning of this section, there still exists some small difference between corresponding points. At the same time, the orthoimages input for change detection are already sampled to the same resolution to facilitate change detection. Based on the above analysis, we conclude on the rotation invariance and scale invariance for the two orthoimages. The location difference between them can be simply described as the shift in X and Y direction. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia We utilize a global matching method to obtain the overall location difference for the whole orthoimage. The computing scheme is described as follows. For two orthoimages ;, and 7, with the same size, any of them can be selected as the comparison target image, for example; . Then initially we put 1, and 7 totally overlapped with each other, and then shift; in both X and Y directions in a defined certain range, [-r, r]. For each shift position the matching cost is computed and the position with the minimum matching cost is decided as the global location difference between two orthoimages. Y lg m, j* n- GJ) arg min, — (D me[-r,r] N mn ne[-r,r] where g,(i, j) ^ intensity of pixel (; j) in 7, gl(i+m,j+n) = intensity of the pixel in shifted I, that corresponds to (j, ;) of 1, N m, "count on the pair of corresponding pixels in shift position of 71,7 m,n = shift position of 7, [=r,r] = shift range of m and n Experimental results of the global matching method show that compared with original datasets the location difference over the whole image is reduced. Especially for the orthoimage showing relatively flat land, there is almost no location difference after the global rectification. In comparison, for the orthoimage including height changing landform, we find that for the regions with various altitudes, there are still remaining location errors, respectively different in each region. This phenomenon happens because for different altitude levels, the ortho-rectification amount on the original images is different, which results in different location difference. To solve this problem, a local matching method is further proposed later. 3.2 Illumination Change Adjustment For change detection, the original images are taken under different conditions like the season, the weather and the shooting time in a day, which result in different illuminations in the images. Even for the same rooftop, its color may appear in quite different ways in the orthoimages of two different times. And this phenomenon leads to many wrong detections of color change. In order to solve this problem, we decide to unify the illumination of the two orthoimages for change detection. There have been many methods to analyze the illumination model of the sun for the aerial images according to the shooting season, the shooting time in the day and sometimes the characteristics of the rooftop material for light. To save the efficiency of the whole processing, rather than such complex model analysis, we decide to undertake the color transfer, to only adjust the color tone of one orthoimage to make it look like another orthoimage. Here we utilize the method of histogram matching [5], which adjusts each color channel based on the global image statistics. In details, for each channel, the following function is designed to transfer each intensity value in the source image to the target image.

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